Teradata

Train operators the world over are expected to work miracles, i.e. never to be late. So, with acute service and availability targets to meet, an efficient maintenance program is important. And data-enabled functionality is a must for Siemens. Reactive maintenance (after an incident) and routine, preventive maintenance with its visual inspections and scheduled exchange of components, are no longer enough. We’ve moved on to more cost-effective, condition-based, predictive maintenance. The actual condition of components is measured via the transfer and remote monitoring of diagnostic sensor data; data which is also used to analyse patterns and trends. This helps predict when a component is likely to fail, so it can be repaired before anything untoward happens. To ensure the commercial sustainability of this approach, Siemens needs to use and re-use existing data, creating a kind of ‘Internet of Trains’. Towards this end, they’re analysing sensor data in near real time, which means they can react very quickly, ensuring that customer transport services aren’t interrupted. “It is really difficult to define every issue before it impacts operations using only data from the trains”, Kress explains. However, recent success stories prove that everything is possible.

The case study presents three different enterprises: a Top-5 Global Retail Bank, a UK Retailer, and a European Multiline Insurer, each facing unique challenges in enhancing their customer experience (CX). The bank was struggling with personalizing CX, requiring more granular detail in their data and analytics, and managing CX across all their digital channels. The UK Retailer was unable to maximize customer relationships due to a lack of insight into their customers' online activities. Their aggregated data was always 24 hours out of date, and they could only infer what customers wanted based on past behavior. The European Multiline Insurer was finding it difficult to capture insights from customers self-serving online. The limited data they had was typically 48 hours old, making it impossible to react to customers in the moment.

The citywide multi-modal transportation authority was facing a significant challenge due to the city's fast-paced development and growing population. The projected demand was expected to strain the public transportation system if not planned for properly. To serve these anticipated needs, the authority drafted its Land Transport Master Plan for transportation network investments that forecasted needs over a decade. In addition to a rapidly growing population, ever-expanding data volumes posed a considerable challenge to their technology infrastructure. The authority relies on data and applications to ensure smooth travel for all—capturing more than 12 million records on public transport each day. However, land transport IT systems were designed for quick response time, with priority given to keeping transactions moving; but not for keeping data beyond three months. Lost data meant a lost ability to conduct meaningful trend analysis, create long-term policy planning, or engage in data mining.

The case study presents five different organizations facing various challenges. A global bank was struggling to engage with customers due to ineffective profitability practices, poor cross-sell and up-sell functions, and inadequate segmentation analyses. A global rental company needed to modernize their analytics ecosystem as their processes were manual, time-consuming, and limited in model calculation. A global retailer had issues with non-product indirect spend, unable to identify non-compliant activity, leading to inconsistencies, reporting delays, and a lack of detail. A global B2B distributor wanted to improve their rebate compliance, but their existing process was manual, time-consuming, and lacked a centralized source of contract terms. Lastly, a global consumer packaged goods (CPG) enterprise was struggling to enforce its travel and entertainment (T&E) policies due to manual recording and reporting processes.

The second largest telecom operator in Pakistan, with 40.7 million active subscribers, 27% data users and approximately 1.13 BN USD revenue, was facing challenges in maintaining its customer base and minimizing churn attributed to poor network services. Customer feedback indicated that poor network experience was the key reason for churn and dissatisfaction. The marketing, sales, and network teams were making decisions in silos, using disparate data sets. The company lacked the technical expertise to integrate, build, and manage analytical capability on large data volumes related to network experience. They needed help in deploying analytics in their network to generate actionable insights.